{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from leaf_area import *\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('LeafCounting/data/dataset_folder_5.npz', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data['arr_0']\n",
    "y = data['arr_1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(74, 240, 240, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x246d3a72c48>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "output, mask = extract_leaf(x[34])\n",
    "plt.imshow(output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.16161458333333334\n"
     ]
    }
   ],
   "source": [
    "print(calculate_leaf_area(\"E:/Dataset/A5/rgb_00_009_05.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.612847222222223\n"
     ]
    }
   ],
   "source": [
    "print(calculate_green_pixel(mask)/(240*240)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = np.load('LeafCounting/data/dataset.npz', allow_pickle=True)\n",
    "(x_train, x_val, x_test, y_train, y_val, y_test) = dataset['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# boundaries for green color\n",
    "boundaries = [\n",
    "    ([33, 50, 50], [90, 255, 255])\n",
    "#     ([103, 86, 65], [145, 133, 128])\n",
    "#     ([20, 0, 0], [100, 180, 176])\n",
    "]\n",
    "(lower, upper) = boundaries[0]\n",
    "lower = np.array(lower, dtype=\"uint8\")\n",
    "upper = np.array(upper, dtype=\"uint8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_noises_mask_test(mask):\n",
    "    element = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))\n",
    "#     mask = cv2.erode(mask, element, iterations=1)\n",
    "#     mask = cv2.dilate(mask, element, iterations=1)\n",
    "#     mask = cv2.erode(mask, element)\n",
    "    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, element)\n",
    "    return mask\n",
    "\n",
    "def extract_leaf_test(np_image):\n",
    "    hsvIm = cv2.cvtColor(np_image, cv2.COLOR_BGR2HSV)\n",
    "    mask = cv2.inRange(hsvIm, lower, upper)\n",
    "#     mask = remove_noises_mask_test(mask)\n",
    "    output = cv2.bitwise_and(np_image, np_image, mask=mask)\n",
    "\n",
    "    return output, mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20adb685e48>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = 3\n",
    "test = x[index].copy()\n",
    "leaf_image, leaf_mask = extract_leaf_test(x[index])\n",
    "plt.imshow(leaf_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = 0\n",
    "test = x[index].copy()\n",
    "leaf_image, leaf_mask = extract_leaf_test(x[index])\n",
    "# centroid = find_centroid(x[index])\n",
    "image = cv2.cvtColor(leaf_image, cv2.COLOR_BGR2GRAY)\n",
    "# edges = cv2.Canny(leaf_mask, 150, 255)\n",
    "ret, thresh = cv2.threshold(leaf_mask, 100, 255, 0)\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_good_contours(contours, threshold):\n",
    "    new_contours = []\n",
    "    for i in range(len(contours)):\n",
    "        if len(contours[i]) >= threshold:\n",
    "            new_contours.append(contours[i])\n",
    "            \n",
    "    return new_contours\n",
    "\n",
    "contours = find_good_contours(contours, 10)\n",
    "len(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(55, 1, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "contours[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20add949508>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = x[index].copy()\n",
    "plt.imshow(cv2.drawContours(test, contours, -1, (0,255,0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20adb758748>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = x[index].copy()\n",
    "mask = np.zeros(test.shape,dtype=\"uint8\")\n",
    "plt.imshow(cv2.drawContours(mask, [contours[0]], -1, 255, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(contours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
